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Advanced Algorithms for Tracing Local Maxima in Folded Materials and Digital Endocasts

This research project, in collaboration with Tim Rowe (UT) and Blaire Van Valkenburgh (UCLA), explores methodologies for tracing local maxima and minima in complex geometries, specifically in thin folded fabrics such as airbag materials. We aim to develop algorithms that effectively account for inconsistencies in gray levels and optimize scanning parameters, with the goal of creating accurate digital endocasts. Initial phases focus on identifying effective separation materials and translating complex folded structures into CAD models while overcoming challenges associated with image processing and data handling.

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Advanced Algorithms for Tracing Local Maxima in Folded Materials and Digital Endocasts

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  1. Surface Specification Solutions Sought • Sheet tracing • Identifiable as local maxima/minima, not boundary • Folded fabric • Nasal turbinates • Mostly-closed volumes; Digital endocasts • (Semi?)-automatically finding closures • Finding surfaces that only exist in the mind of the scientist…

  2. Nasal turbinates Project with Tim Rowe (UT) and Blaire Van Valkenburgh (UCLA)

  3. Airbag fabric

  4. Airbag Folds • Phase 1: Find a good separator material • Need to keep plies distinct when folded together • Scanning conditions • 420 kV, 1.8 mA • Field of view 180 mm • Slice thickness 0.25 mm • Slice spacing 0.2 mm • 30s/slice

  5. Airbag folds

  6. Other difficulties • Most algorithms (i.e. Marching Cubes) trace interfaces, not local maxima/minima • Would provide a “hollow plane” • Thinness of sheet • Ensures gray levels inconsistent • Fouls up image processing for noise reduction

  7. Airbag folds • Phase 2: Conversion to CAD • The tricky part No good threshold value

  8. Airbag folds • Folded sheet traversal algorithm • Based on local moment of inertia

  9. Algorithm Outline • Build using facets (triangles in 3D space) • Use voxel centers as vertices • Find first facet, and then grow out from each edge • Growth is normal to edge surface, and perpendicular to 3rd eigenvector of endpoints • Search cone to find best target (highest gray value) • Add best facet (highest gray new voxel) at each step • No concave angles between facets • After facet added, see if new voxel can resolve any other edges

  10. Algorithm in action

  11. Remaining issues • Speed • Substantial data sets would take hours • Filling holes • Avoiding ragged edges • ???? • As more data sets are tried, additional complexities may be revealed • Bag seams?

  12. “Lunch bag” scan

  13. Digital Endocasts

  14. Your mission, if you choose…

  15. Extra credit

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